An Adaptive Color Image Segmentation

A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also

Enhancement of Image Resolution by Binarization

Image segmentation is one of the principal approaches of image processing. The choice of the most appropriate Binarization algorithm for each case proved to be a very interesting procedure itself. In this paper, we have done the comparison study between the various algorithms based on Binarization algorithms and propose a methodologies for the validation of Binarization algorithms. In this work we have developed two novel algorithms to determine threshold values for the pixels value of the gray scale image. The performance estimation of the algorithm utilizes test images with, the evaluation metrics for Binarization of textual and synthetic images. We have achieved better resolution of the image by using the Binarization method of optimum thresholding techniques.Comment: 5 pages, 8 figure

An Adaptive Color Image Segmentation

A novel Adaptive Color Image Segmentation (ACIS) System for color image segmentation is presented. The proposed ACIS system uses a neural network with architecture similar to the multilayer perceptron (MLP) network. The main difference is that neurons here uses a multisigmoid activation function. The multisigmoid function is the key for segmentation. The number of steps i.e. thresholds in the multisigmoid function are dependant on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSV color space. Here, the main use of neural network is to detect the number of objects automatically from an image. The advantage of this method is that no a priori knowledge is required to segment the color image. ACIS label the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images. Experimental results show that the performance of ACIS is robust on noisy images also

Improved support vector clustering algorithm for color image segmentation

Color image segmentation has attracted more and more attention in various application fields during the past few years. Essentially speaking, color image segmentation problem is a process of clustering according to the color of pixels. But, traditional clustering methods do not scale well with the number of training sample, which limits the ability of handling massive data effectively. With the utilization of an improved approximate Minimum Enclosing Ball algorithm, this article develops an fast support vector clustering algorithm for computing the different clusters of given color images in kernel-introduced space to segment the color images. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to other popular algorithms, it has the competitive performances both on training time and accuracy. Color image segmentation experiments on both synthetic and real-world data sets demonstrate the validity of the proposed algorithm

Multilevel minimum cross entropy threshold selection based on particle swarm optimization

Abstract Thresholding is one of the popular and fundamental techniques for conducting image segmentation. Many thresholding techniques have been proposed in the literature. Among them, the minimum cross entropy thresholding (MCET) have been widely adopted. Although the MCET method is effective in the bilevel thresholding case, it could be very time-consuming in the multilevel thresholding scenario for more complex image analysis. This paper first presents a recursive programming technique which reduces an order of magnitude for computing the MCET objective function. Then, a particle swarm optimization (PSO) algorithm is proposed for searching the near-optimal MCET thresholds. The experimental results manifest that the proposed PSO-based algorithm can derive multiple MCET thresholds which are very close to the optimal ones examined by the exhaustive search method. The convergence of the proposed method is analyzed mathematically and the results validate that the proposed method is efficient and is suited for real-time applications

A comparative study of algorithms for automatic segmentation of dermoscopic images

Melanoma is the most common as well as the most dangerous type of skin cancer. Nevertheless, it can be effectively treated if detected early. Dermoscopy is one of the major non-invasive imaging techniques for the diagnosis of skin lesions. The computer-aided diagnosis based on the processing of dermoscopic images aims to reduce the subjectivity and time-consuming analysis related to traditional diagnosis. The first step of automatic diagnosis is image segmentation. In this project, the implementation and evaluation of several methods were proposed for the automatic segmentation of lesion regions in dermoscopic images, along with the corresponding implemented phases for image preprocessing and postprocessing. The developed algorithms include methods based on different state of the art techniques. The main groups of techniques which have been selected to be studied and implemented are thresholding-based methods, region-based methods, segmentation based on deformable models, as well as a new proposed approach based on the bag-of-words model. The implemented methods incorporate modifications for a better adaptation to features associated with dermoscopic images. Each implemented method was applied to a database constituted by 724 dermoscopic images. The output of the automatic segmentation procedure for each image was compared with the corresponding manual segmentation in order to evaluate the performance. The comparison between algorithms was carried out regarding the obtained evaluation metrics. The best results were achieved by the combination of region-based segmentation based on the multi-region adaptation of the k-means algorithm and the subIngeniería de Sistemas Audiovisuale

Reliable and Automatic Recognition of Leaf Disease Detection using Optimal Monarch Ant Lion Recurrent Learning

Around 7.5 billion people worldwide depend on agriculture production for their livelihood, making it an essential component in keeping life alive on the planet. Negative impacts are being caused on the agroecosystem due to the rapid increase in the use of chemicals to combat plant diseases. These chemicals include fungicides, bactericides, and insecticides. Both the quantity and quality of the output are impacted when there is a high-scale prevalence of diseases in crops. Plant diseases provide a significant obstacle for the agricultural industry, which has a negative impact on the growth of plants and the output of crops. The problem of early detection and diagnosis of diseases can be solved for the benefit of the farming community by employing a method that is both quick and reliable regularly. This article proposes a model for the detection and diagnosis of leaf infection called the Automatic Optimal Monarch AntLion Recurrent Learning (MALRL) model, which attains a greater authenticity. The design of a hybrid version of the Monarch Butter Fly optimization algorithm and the AntLion Optimization Algorithm is incorporated into the MALRL technique that has been proposed. In the leaf image, it is used to determine acceptable aspects of impacted regions. After that, the optimal characteristics are used to aid the Long Short Term Neural Network (LSTM) classifier to speed up the process of lung disease categorization. The experiment's findings are analyzed and compared to those of ANN, CNN, and DNN. The proposed method was successful in achieving a high level of accuracy when detecting leaf disease for images of healthy leaves in comparison to other conventional methods